
"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}



class 			lowercase					( lowercase__		):
			lowercase        =     '''roc_bert'''






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Tuple=30_522      ,SCREAMING_SNAKE_CASE_  :	List[str]=768      ,SCREAMING_SNAKE_CASE_  :	str=12      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=12      ,SCREAMING_SNAKE_CASE_  :	int=3_072      ,SCREAMING_SNAKE_CASE_  :	Dict="gelu"      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=512      ,SCREAMING_SNAKE_CASE_  :	Tuple=2      ,SCREAMING_SNAKE_CASE_  :	Dict=0.02      ,SCREAMING_SNAKE_CASE_  :	int=1e-12      ,SCREAMING_SNAKE_CASE_  :	Dict=True      ,SCREAMING_SNAKE_CASE_  :	str=0      ,SCREAMING_SNAKE_CASE_  :	Any="absolute"      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_  :	int=768      ,SCREAMING_SNAKE_CASE_  :	str=910      ,SCREAMING_SNAKE_CASE_  :	int=512      ,SCREAMING_SNAKE_CASE_  :	Dict=24_858      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=True      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  max_position_embeddings
				lowerCAmelCase												=  hidden_size
				lowerCAmelCase												=  num_hidden_layers
				lowerCAmelCase												=  num_attention_heads
				lowerCAmelCase												=  intermediate_size
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  type_vocab_size
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  use_cache
				lowerCAmelCase												=  enable_pronunciation
				lowerCAmelCase												=  enable_shape
				lowerCAmelCase												=  pronunciation_embed_dim
				lowerCAmelCase												=  pronunciation_vocab_size
				lowerCAmelCase												=  shape_embed_dim
				lowerCAmelCase												=  shape_vocab_size
				lowerCAmelCase												=  concat_input
				lowerCAmelCase												=  position_embedding_type
				lowerCAmelCase												=  classifier_dropout
				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


